A couple of related questions: for most current crowd sourced problem solving endeavors, how “deep” does the problem solving routinely go? And do the results meaningfully change if incentives are introduced?

It was a good, thoughtful question. I answered it in the comments there, and wanted to make the answer into its own blog post, below.

The depth of the problem-solving in a crowdsourcing endeavor is wholly dependent on:

The question that is asked

The engagement of the question sponsor

Who is asked to participate

Why people would want to participate

A few points on each of those factors.

Question that is asked

As you can imagine, the question impacts the depth of problem-solving. In-depth question = in-depth problem-solving. The more specific the question, the better the quality of people’s contributions. “Specific” here doesn’t mean asking a tactical, low-level question. Rather, it means clearly delineating what is sought in a way that people can relate to .

Engagement of the question sponsor

Crowdsourcing works best (obviously?) when solving a specific problem that someone has. People will respond to the question with different concepts and questions. The feedback of the question asker (aka “sponsor”) provides the back-n-forth that breaks through initial responses to build a deeper response.

Who is asked to participate

Getting cognitive diversity is the key, as described in the post. But also, you want people who have some connection and interest in the question. Think holistically about that. Upstream, downstream, adjacent fields. Problem-solving depth requires matching a question with people who will give a damn.

Why people would want to participate

The question of “why” is closely related to the preceding question of “who”. If a question’s answer potentially affects a person, there is built-in motivation to participate: steer things in a way that makes sense to you. This works well for internal employee-based crowdsourcing. However, there are certainly questions where the personal impact may be less acute. Other incentives come in to play. Engagement with a sponsor – with attendant acknowledgments, thank you’s, feedback – are great incentives. Opportunities to see an idea through is a powerful stimulant. And prizes have great power. Prizes work best when they establish an opportunity to see an idea one is passionate about become real (e.g. investment funds). Or when the question is not one that directly impacts you. In such a case, they are compensation for putting your brainpower to work problem-solving.

I’d bet most of us understand an the initially proposed idea and its ultimate implementation are going to differ. Ideas are cheap, as they say. It’s what happens after the idea is proposed where success or failure is determined. Typically, the “after proposal” focus is on the execution of the idea. But there’s a phase between the idea proposal and the execution of it. It’s a phase where the idea is molded and sharpened.

An idea essentially goes through a journey prior to its implementation:

The probability of an idea becoming reality is affected by different types of participation. Four different personalities act of the idea during its journey:

When will a customer decide your innovative product or service is worth adopting? It’s a question that marketers, strategists and others spend plenty of time thinking about. The factors are myriad and diverse. In this post, let’s examine two primary elements that influence both if an innovation will be adopted, and when it would happen:

Decision weights assigned to probabilities

Probability of job-to-be-done improvement

A quick primer on both factors follows. These factors are then mapped to the innovation adoption curve. Finally, they are used to analyze the adoption of smartwatches and DVRS.

Decision weights assigned to probabilities

Let’s start with decision weights, as that’s probably new for many of us. In his excellent book, Thinking, Fast and Slow, Nobel laureate Daniel Kahneman describes research he and a colleague did that examined the way people think about probabilities. Specifically, given different probabilities for a gain, how do people weight those probabilities?

Why?

Classic economics indicates that an outcome has a 25% probability, then 25% is the weight a rational person should assign to that outcome. If you’ve taken economics or statistics, you may recall being taught something along these lines. However, Kahneman and his colleague had anecdotally seen evidence that people didn’t act that way. So they conducted field experiments to determine how people actually incorporated probabilities into their decision making. The table below summarizes their findings:

The left side of the table shows that people assign greater weight to low probabilities than they should. Kahneman calls this the possibility effect. The mere fact that something could potentially happen has a disproportionate weight in decision-making. Maybe we should call this the “hope multiplier”. It’s strongest at the low end, with the effect eroding as probabilities increase. When the probability of a given outcome increases to 50% and beyond, we see the emergence of the uncertainty effect. In this case, the fact that something might not happen starts to loom larger in our psyche. This is because we are loss averse. We prefer avoiding losses to acquiring gains.

Because of loss aversion, an outcome that has an 80% probability isn’t weighted that way by people. We look at that 20% possibility that something will not happen (essentially a “loss”), and fear of that looms large. We thus discount the 80% probability to a too-low decision weight of 60.1.

Probability of job-to-be-done improvement

A job-to-be-done is something we want to accomplish. It consists of individual tasks and our expectation for each of those tasks. You rate the fulfillment of the expectations to determine how satisfied you are with a given job-to-be-done. This assessment is a cornerstone of the “job-to-be-done improvement” function:

Dissatisfaction: How far away from customers’ expectations is the incumbent way that they fulfill a job-to-be-done? The further away, the greater the dissatisfaction. This analysis is really dependent on the relative importance of the individual job tasks. More important tasks have greater influence on the overall level of satisfaction.

Solution improvement: How does the proposed innovation (product, service) address the entirety of the existing job? It will be replacing at least some, if not all, of the incumbent solution. What are the better ways it fulfills the different job tasks?

Cost: How much does the innovation cost? There’s the out-of-pocket expense. But there are other costs as well. Learning costs. Things you cannot do with the new solution that you currently can. The costs will be balanced against the increased satisfaction the new solution delivers.

These three elements are the basis of determining the fit with a given job-to-be-done. Because of their complexities, determining precise measures for each is challenging. But it is reasonable to assert a probability. In this case, the probability that the proposed solution will provide a superior experience to the incumbent solution.

Mapping decision weights across the innovation adoption curve

The decision weights described earlier are an average across a population. There is variance in those. The decision weights for each probability of gain in job-to-be-done will differ by adoption segment, as shown below:

The green and red bars along the bottom of each segment indicate the different weights assigned to the same probabilities for each segment. For Innovators and Early Adopters, any possibility of an improvement in job-to-be-done satisfaction is overweighted significantly. At the right end, Laggards are hard-pressed to assign sufficient decision weights to anything but an absolutely certain probability of increased satisfaction.

Studies have shown that our preferences for risk-aversion and risk-seeking are at least somewhat genetically driven. My own experience also says that there can be variance in when you’re risk averse or not. It depends on the arena and your own experience in it. I believe each of us has a baseline of risk tolerance, and we vary from that baseline depending on circumstances.

Two cases in point: smartwatches and DVRs

The two factors – decision weights and probability of improved job-to-be-done satisfaction – work in tandem to determine how far the reach of a new innovation will go. Generally,

If the probability of job-to-be-done improvement is low, you’re playing primarily to the eternal optimists, Innovators and Early Adopters.

If the probability of improvement is high, reach will be farther but steps are needed to get later segments aware of the benefits, and to even alter their decision weights.

Let’s look at two innovations in the context of these factors.

Smartwatches

Smartwatches have a cool factor. If you think of a long-term trend of progressively smaller computing devices – mainframes, minicomputers, desktops, laptops, mobile devices – then the emergence of smartwatches is the logical next wave. Finally, it’s Dick Tracy time.

The challenge for the current generation of smartwatches is distinguishing themselves from the incumbent solution for people, smartphones. Not regular time wristwatches. But smartphones. How much do smartwatches improve the jobs-to-be-done currently fulfilled by smartphones?

Some key jobs-to-be-done by smartphones today:

Email

Texting

Calls

Social apps (Facebook, Twitter, etc.)

Navigation

Games

Many, many more

When you consider current smartphone functionality, what job tasks are under-satisfied? In a Twitter discussion about smartwatches, the most compelling proposition was that the watch makes it easier to see updates as soon as they happen. Eliminate the pain of taking your phone out of your pocket or purse. Better satisfaction of the task of knowing when, who and what for emails, texts, social updates, etc.

But improvement in this task comes at a cost. David Breger wrote that he had to stop wearing his smartwatch. Why? The updates pulled his eyes to his watch. Constantly. To the point where his conversational companions noticed, affecting their interactions. What had been an improvement came with its own cost. There are, of course, those people who bury their faces in their phones wherever they are. The smartwatch is a win for them.

If I were to ballpark the probability that a smartwatch will deliver improvement in its targeted jobs-to-be-done, I’d say it’s 20%. Still, that’s good enough for the Innovators segment. I imagine their decision weights look something like this:

The mere possibility of improvement drives these early tryers-of-new-things. It explains who was behind Pebble’s successful Kickstarter campaign. But the low probability of improving the targeted jobs-to-be-done dooms the smartwatch, as currently conceived, to the left side of the adoption curve.

DVRs

Digital video recorders make television viewing easier. Much easier. Back when TiVo was the primary game in town, early adopters passionately described how incredible the DVR was. It was life-changing. I recall hearing the praise back then, and I admit I rolled my eyes at these loons.

Not so these days.

DVRs have become more commonplace. With good reason. They offer a number of features which improve various aspects of the television viewing job-to-be-done:

But there are costs. If you’ve got a big investment in VCR tapes or DVDs, you want to play those. It does cost money to purchase a DVR plan. The storage of the DVR has a ceiling. You have to learn how to set up and work with a DVR. It becomes part of the room decor. What happens if the storage drive crashes?

My estimate is that the DVR has an 80% probability of being better than incumbent solutions. Indeed, this has been recognized in the market. A recent survey estimates U.S. household adoption of DVRs at 44%. Basically, knocking on the door of the Late Majority. I imagine their decision weights look like this:

On the probability side of the ledger, they will need to experience DVRs themselves to understand its potential. For the Late Majority, this happens through experiencing an innovation through their Early Majority friends. They become aware of how much an innovation can improve their satisfaction.

On the decision weight, vendors must do the work of addressing the uncertainty that comes with the innovation. This means understanding the forces – allegiance to the incumbent solution, anxiety about the proposed solution – that must be overcome.

Two influential factors

As you consider your product or service innovation, pay attention to these two factors. The first – jobs-to-be-done – is central to getting adoption of any thing. without the proper spade work there, you will be flying blind into the market. The second factor is our human psyche, and how we harbor hope (possibility) and fear (uncertainty). Because people are geared differently, you’ll need to construct strategies (communication channels, messaging, product enhancements) that pull people toward your idea, overcoming their natural risk aversion.

Crowdsourcing is a method of solving problems through the distributed contributions of multiple people. It’s used to address tough problems that happen everyday. Ideas for new opportunities. Ways to solve problems. Uncovering an existing approach that addresses your need.

Time and again, crowdsourcing has been used successfully to solve challenges. But…why does it work? What’s the magic? What gives it an advantage over talking with your pals at work, or doing some brainstorming on your own? In a word: diversity. Cognitive diversity. Specifically these two principles:

Diverse inputs drive superior solutions

Cognitive diversity requires spanning gaps in social networks

These two principles work in tandem to deliver results.

Diverse inputs drive superior solutions

When trying to solve a challenge, what is the probability that any one person will have the best solution for it? It’s a simple mathematical reality: the odds of any single person providing the top answer are low.

How do we get around this? Partly by more participants; increased shots on goal. But even more important is diversity of thinking. People contributing based on their diverse cognitive toolkits:

As described by University of Michigan Professor Scott Page in The Difference, our cognitive toolkits consist of: different knowledge, perspectives and heuristics (problem-solving methods). Tapping into people’s cognitive toolkits brings fresh perspectives and novel approaches to solving a challenge. Indeed, a research study found that the probability of solving tough scientific challenges is three times higher if a person’s field of expertise is seven degrees outside the domain of the problem.

In another study, researchers analyzed the results of an online protein-folding game, Foldit. Proteins fold themselves, but no one understands how they do so. This is particularly true of experts in the field of biochemistry. So the online game allows users to simulate it, with an eye towards better understanding the ways the proteins fold themselves. As reported by Andrew McAfee, the top players of Foldit were better than both computers and experts in the field at understanding the folding sequence. The surprising finding? None had taken chemistry beyond a high school course. It turns out spatial skills are more important to solve the problem than deep domain knowledge of proteins.

Those two examples provide real-world proof for the models and solution-seeking benefits of cognitive diversity described by Professor Page.

Problem solving can be thought of as building a solutions landscape, planted with different ideas. Each person achieves their local optimum, submitting the best idea they can for a given challenge based on their cognitive assets.

But here’s the rub: any one person’s idea is unlikely to be the best one that could be uncovered. This makes sense as both a probabilistic outcome, and based on our own experiences. However in aggregate, some ideas will stand out clearly from the rest. Cognitive diversity is the fertile ground where these best ideas will sprout.

In addition to being a source of novel ideas, cognitive diversity is incredibly valuable as feedback on others’ ideas. Ideas are improved as people contribute their distinct points of view. The initial idea is the seedling, and feedback provides the nutrients that allow it to grow.

Cognitive diversity requires spanning gaps in social networks

Cognitive diversity clearly has a significant positive effect on problem-solving. Generally when something has proven value to outcomes, companies adopt it as a key operating principle. Yet getting this diversity has not proven to be as easy and common as one might expect.

Why?

Because it’s dependent on human behavior. Left to our own devices, we tend to turn to our close connections for advice and feedback. These strongties are the core of our day-in, day-out interactions.

But this natural human tendency to turn to our strong ties is why companies are challenged to leverage their cognitive diversity. University of Chicago Professor Ron Burt describes the issue as one of structural holes between nodes in a corporate social network in his paper, Structural Holes and Good Ideas (pdf). A structural hole is a gap between different groups in the organization. Information does not flow across structural holes.

In and of themselves, structural holes are not the problem. Rather, the issue is that when people operate primarily within their own node, their information sources are redundant. Over time, the people in the node know the same facts, develop the same assumptions and optimize to work together in harmony. Sort of like a silo of social ties.

The impact of this is a severe curtailment of fresh thinking, which impacts the quality of ideas. Professor Burt found empirical evidence for this in a study of Raytheon’s Supply Chain Group. 673 employees were characterized by their social network connections, plotting them on a spectrum from insular to diverse. These employees then provided one idea to improve supply chain management at Raytheon. Their ideas were then assessed by two senior executives.

The results? Employees with more diverse social connections provided higher quality ideas. To the right is a graph of the rated ideas, with a curve based on the average idea ratings versus the submitter’s level of network diversity. The curve shows that with each increase in the diversity of a person’s connections, the higher the value of their idea.

Employees with access to diverse sources of information provided better ideas. Their access to nonredundant information allowed them to generate more novel, higher potential ideas. Inside organizations, there are employees who excel at making diverse connections across the organization. These people are the ones who will provide better ideas. They are brokers across the structural holes in social networks.

Professor Burt provides the key insight about these brokers:

People connected to groups beyond their own can expect to ﬁnd themselves delivering valuable ideas, seeming to be gifted with creativity. This is not creativity born of genius; it is creativity as an import-export business. An idea mundane in one group can be a valuable insight in another.

An “import-export business”. Consider that for a moment. It’s a metaphor that well describes the key value of the brokers. They are exchange mechanisms for cognitive diversity. They are incredibly valuable to moving things forward inside organizations. But are organizations overly dependent on these super-connectors? Yes. Companies are leaving millions on the table by not enabling a more scalable, comprehensive and efficient means for exchanges of cognitive diversity.

Would if we could systematize what the most connected employees do?

Crowdsourcing doesn’t eliminate the need for the super-connectors. They play a number of valuable roles inside organizations. But by crowdsourcing to solve problems, companies gain the following:

Deeper reach into the cognitive assets of all employees

Avoiding the strong ties trap of problem-solving

Faster surfacing of the best insights

Neutralize the biases that the super-connectors naturally have

As you consider ways to improve your decision-making and to foster greater cross-organizational collaboration, make crowdsourcing a key element of your strategic approach.

It is my pleasure and honor to announce that today I’ve joined HYPE Innovation as a full-time Senior Consultant. HYPE provides an enterprise innovation management software platform – HYPE Enterprise – used by large companies around the globe. In my consulting role, I’ll be working hands-on with customers across the phases of innovation maturity:

Beginning the journey toward a more collaborative innovation approach

Expanding usage as they gain experience and see results

Developing advanced ecosystems to drive next generation business models and products

This role is a change for me, moving from product to consulting. But it’s one I embrace and I’m looking forward to. I’ve talked a lot here about the need to understand customers’ jobs-to-be-done. By working side-by-side with organizations, I’m going to have a deep understanding of their jobs-to-be-done for innovation and problem-solving. And even better, an opportunity to help make them successful.

HYPE is headquartered in Bonn, Germany, and I’ll be working from San Francisco. In this post, I want to cover two areas:

State of the innovation management market

What makes HYPE special

State of innovation management market

Enterprise traction

Over the past five years, I’ve worked with a number of customers and thought leaders in the innovation management space. People that are committed to and passionate about this. The first thing to know is that enterprises are actively exploring ways to be better at innovating. Many, many of the companies you know and buy products and services from. From its roots as online suggestions boxes, innovation management has become a full-fledged corporate discipline. In fact, research firm IDC forecasts that by the end of 2016, 60% of the Fortune 500 will be using social-enabled innovation management solutions. Which, if you follow the innovation diffusion lifecycle, means we’ll start to see the late majority taking it up.

Focused ideation

When I began working in the innovation field, the primary use case for innovation management software was to be an open suggestion box, equipped with social features (visibility, commenting, voting). Anytime someone had an idea, they had a place to post it. Unfortunately, that approach proved limited in engagement and value. Thus, that model has changed significantly the past few years. Organizations are now running campaigns that target narrow, specific topics. They are time-boxed events, which in a broad sense is a form of game mechanic that spurs greater participation. Campaigns offer these advantages:

Continuously refreshing the program and reason for people to participate

Address specific organization needs

Beyond innovation

Innovation – however you define it – continues to be a prominent use case. And with good reason, as CEOs rate it a top priority. There are multiple disciplines that address innovation: crowdsourcing, design thinking, TRIZ, incubators, lean startup, etc. Generally, innovation is considered creating something new which adds value.

But I’m seeing signs that crowdsourcing is being applied in other ways outside the traditional view of innovation. Here are three examples:

Problem-solving: An example of this is cost-saving initiatives. People out on the front lines are seeing opportunities for improvement that are hidden from decision-makers in the headquarters.

Positive deviance: In every large organization, there are people who have figured out a different, better way to do something. Crowdsourcing helps find these people, and their novel approaches can be identified and shared.

Trend-spotting: With an army of employees out in the field, organizations have a ready way to canvas an area. People can post what they’re seeing, a valuable source of raw insight.

Idea development, evaluation and selection take center stage

When I talk with people not familiar with the innovation management field, I find their understanding often to be, “Oh, so it’s an idea collection app.” That is a necessary feature of course – no ideas, no innovation. But it’s a comical under-representation of what innovation management is. As Professor Tim Kastelle notes:

“Generating ideas is the easiest part. Most organisations already have enough ideas. The challenge for them is not generating more but implementing their existing ideas more effectively.”

As the market matures, companies are seeking ways to better advance the most promising ideas. This is where the puck’s heading.

Innovation becomes part of the purposeful collaboration canon

In the broader enterprise 2.0 social business market, the integration of ‘social’ into core business functions has emerged as the basis of value. This is a change from the movement’s early roots. Constellation Research VP Alan Lepofsky nicely illustrates this evolution to Generation 3 as follows:

Innovation is a prominent use case that benefits from the application of social and collaboration. You can see more in Alan’s Slideshare presentation on innovation and purposeful collaboration.

What makes HYPE special

From my experience in the industry and in my meetings with the team, three things about HYPE stand out in the innovation management field

Singular focus on customers’ innovation jobs-to-be-done

Market leadership

Demonstrated customer excellence

Singular focus on customers’ innovation jobs-to-be-done

HYPE has over a decade of experience in the innovation market. It’s roots were in the R&D world, with a deep emphasis on how to maximize the value of ideas. In industry parlance, this is sometimes called the “back-end” of innovation. It’s a sophisticated activity with variance in process for each organization. Through the years of working with customers, HYPE has become adept at handling this phase of innovation. I know it’s not easy – I did some initial product work myself in this realm previously. Success here hinges on understanding what customers seek to achieve, and acting on it.

With the rise of social business and increased interest in better utilizing the collective smarts of employees, HYPE moved forward to the “front-end” of innovation. Powerful features include campaign development, participation management, idea surfacing, collaboration and evaluation. With this investment of time and effort, HYPE offers the most functional full-cycle innovation process in the industry:

With deep expertise built throughout the platform, HYPE is well-positioned to address organizations’ innovation jobs-to-be-done.

Market leadership

In the past few years, HYPE has increased its presence in the market, following an investment from ViewPoint Capital Partners. From its roots in Germany, the company has become the leader in Europe. It is now seeing good growth in broader EMEA, the United States and South America.

HYPE achieved the top overall ranking, the coveted “top right” position of the Wave.

Demonstrated customer excellence

HYPE has over 170 customers from around the world. Consistent with my experience, the industries are varied. Some representative names are shown to the left. This is something one sees when it comes to innovation: everyone does it. There’s really not a specific sector that pursues innovation and problem-solving more than others.

HYPE has a number of long-term relationships. And it’s fair to say that once you’re a client of HYPE, you’ll be happy, satisfied and get results. Annual churn is less than 4%. On a monthly basis, that’s roughly 0.3%, at the magic level for enterprise software companies.

That level of customer satisfaction doesn’t “just happen”. Rather, it comes from being dedicated to customers’ success and working to make them successful at their jobs-to-be-done.

That HYPE logo?

Finally, about the HYPE logo. I actually do not yet know the background on it. But take a look at it. See some similarities to different hand gestures?